Method and system for vehicular collision reconstruction

ABSTRACT

A system for accident reconstruction can include and/or be configured to interface with any or all of: a set of models, a set of modules, a processing system, client application, a user device (equivalently referred to herein as a mobile device), a set of sensors, a vehicle, and/or any other suitable components. A method for accident reconstruction includes collecting a set of inputs; detecting a collision and/or one or more features of the collision; reconstructing the collision; and producing an output based on the reconstruction. Additionally or alternatively, the method can include training a set of models and/or modules, and/or any other suitable processes.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional Application No.62/964,559, filed 22 Jan. 2020, which is incorporated in its entirety bythis reference.

TECHNICAL FIELD

This invention relates generally to the vehicle telematics field, andmore specifically to a new and useful system and method for collisionreconstruction in the vehicle telematics field.

BACKGROUND

In the event of a vehicular accident, a conventional action—inconstructing an insurance claim, for instance—is to reconstruct theaccident in order to determine such information as who and/or what is atfault as well as the extent of the damage.

Conventional methods for accident reconstruction are performedcompletely or nearly completely manually, and often with a very limited,unreliable, and/or inconsistent set of information. The most usefulinformation with which to accurately and objectively assess the accidentis currently conventionally unavailable to those reconstructing theaccident, and even if a portion is available, it is difficult to collectand assess. Furthermore, the overall accident reconstruction process isconventionally slow and tedious, made up of highly manual, human-centricprocesses.

In a variation of a conventional accident reconstruction process (e.g.,as shown in FIG. 8A), for instance, the process starts with an insurancecompany receiving a claim in response to the occurrence of an accident.Often days later, the driver is contacted, and an investigation into thefactors involved in the accident (e.g., the involvement of anothervehicle, the involvement of a pedestrian, towing of a vehicle, policeinvolvement, etc.) begins, often when relevant and/or helpfulinformation is already forgotten, now biased, or otherwise lessreliable. A first notice of loss (equivalently referred to herein as afirst notification of loss) is then produced, which assesses the overallmagnitude and/or type of damage resulting from the accident. In an eventthat the accident only involves property loss, a trip to an auto body isprompted. In an event that the accident analysis is more complex, aninvestigation into potential fraud, manipulation of memory, and/or othercauses for inaccurate information may be prompted. These investigations,however—in addition to being slow—are most often not precise and can beheavily biased as well as slow moving.

Thus, there is a need in the vehicle telematics field to create animproved and useful system and method for reconstructing vehicularaccidents.

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1 is a schematic representation of a method for collisionreconstruction.

FIG. 2 is a schematic representation of a set of models and modules usedin detecting and/or reconstructing a collision.

FIG. 3 depicts an example for illustrative purposes of a set ofprobabilistic outputs produced by a set of classifiers and a set outputsused to reconstruct a collision produced by a set of modules.

FIGS. 4A-4B depict an example for illustrative purposes of a set ofinputs collected over multiple time points and their use in a set ofmodels and modules.

FIG. 5 is a schematic representation of the system for accidentdetection and/or reconstruction.

FIGS. 6A-6B depict examples of a notification presented to a driver at amobile device of the driver.

FIGS. 7A-7B depict examples of a notification presented to a driver at amobile device of the driver.

FIGS. 8A-8B depict variations of a conventional claim insurance processand a variation of a claim insurance process performed with the method200, respectively.

FIGS. 9A-9G depict a variation of a collision reconstruction dashboard(e.g., as produced in S230).

DESCRIPTION OF THE PREFERRED EMBODIMENTS

The following description of the preferred embodiments of the inventionis not intended to limit the invention to these preferred embodiments,but rather to enable any person skilled in the art to make and use thisinvention.

1. Overview

As shown in FIG. 2, a system 100 for accident reconstruction can includeand/or be configured to interface with any or all of: a set of models, aset of modules, a processing system, client application, a user device(equivalently referred to herein as a mobile device), a set of sensors,a vehicle, and/or any other suitable components.

The system can additionally or alternatively include any or all of thesystems, components, embodiments, and examples described in any or allof: U.S. application Ser. No. 15/243,565, filed 22 Aug. 2016; U.S.application Ser. No. 14/566,408, filed 10 Dec. 2014; U.S. applicationSer. No. 14/206,721, filed 12 Mar. 2014; and U.S. application Ser. No.17/111,299, filed 3 Dec. 2020, each of which is incorporated herein inits entirety by this reference.

As shown in FIG. 1, the method 200 for accident reconstruction includescollecting a set of inputs S205; detecting a collision and/or one ormore features of the collision S210; reconstructing the collision S220;and producing an output based on the reconstruction S230. Additionallyor alternatively, the method 200 can include training a set of modelsand/or modules, and/or any other suitable processes.

Further additionally or alternatively, the method 200 can include anyother suitable processes and/or any or all of the methods, processes,embodiments, and examples described in any or all of: U.S. applicationSer. No. 15/243,565, filed 22 Aug. 2016; and U.S. application Ser. No.14/566,408, filed 10 Dec. 2014; U.S. application Ser. No. 15/702,601,filed 12 Sep. 2017; U.S. application Ser. No. 15/835,284, filed 7 Dec.2017; U.S. application Ser. No. 15/401,761, filed 9 Jan. 2017; U.S.application Ser. No. 16/022,120, filed 28 Jun. 2018; U.S. applicationSer. No. 16/022,184, filed 28 Jun. 2018; U.S. application Ser. No.16/166,895, filed 22 Oct. 2018; U.S. application Ser. No. 16/201,955,filed 27 Nov. 2018; U.S. application Ser. No. 17/700,991, filed 2 Dec.2019; U.S. application Ser. No. 14/206,721, filed 12 Mar. 2014; and U.S.application Ser. No. 17/111,299, filed 3 Dec. 2020, each of which ishereby incorporated in its entirety by this reference.

The method 200 is preferably performed with a system 100 as describedabove, but can additionally or alternatively be performed with any othersuitable system(s).

2. Benefits

The system and method for collision reconstruction can confer severalbenefits over current systems and methods.

First, in some variations, the system and/or method confers the benefitof easily and promptly accessing reliable and relevant informationassociated with a vehicular accident. In specific examples, forinstance, the system and method leverage any or all of the followinginformation associated with one or more vehicles involved in an accidentand collected, at least partially, at a mobile device to make aquantitative assessment of the vehicular accident: location information,motion information, driver information, passenger information,circumstantial information, and/or any other suitable information. Thiscan further function to remove or minimize the use of unreliable humanaccounts in the reconstruction of an accident. Additionally oralternatively, any or all of the information can be used to trigger aresponse and/or action to help a driver involved in a collision, such asa life-saving response (e.g., notifying emergency services, notifyingthe police, etc.), assistance in performing one or more tasks and/oreliminating one or more tasks typically required to be performed by thedriver (e.g., by completing and/or assisting in and/or eliminatingpaperwork required by the driver's insurance company, by automaticallyrecommending and/or contacting a body shop in response to a collision,etc.), and/or performing any other suitable functions.

Second, in some variations, additional or alternative to the first, thesystem and/or method confers the benefit of decreasing the time requiredto reconstruct an accident and subsequently the time required to produceone or more outputs (e.g., insurance claim, auto body work approval,etc.) associated with an accident. In specific examples, for instance,information associated with the accident is collected in real time ornear real time (e.g., within seconds, within less than a minute, withinless than an hour, etc.) from a mobile device associated with theaccident (e.g., a driver's mobile phone), and used to at least partiallyassess the accident.

Third, in some variations, additional or alternative to those describedabove, the system and/or method confers the benefit of producing anaccurate, easily interpretable output reconstructing the accident. Inspecific examples, for instance, the method produces an easyintelligible user interface (e.g., at a client application) and/or setof materials (e.g., slide deck, visuals, reports, etc.) with whichinsurance companies can view one or more parameters (e.g., G-forces,speeds, etc.) associated with an accident. Additionally oralternatively, the system and/or method can determine and/or autofillone or more sections of an insurance report, determine and/or autofillany or all of a claim adjustment, determine and transmit a set ofparameters associated with the collision (e.g., as determined by a modeland/or set of modules as described below) to an insurance entity, and/orcan perform any other suitable functions.

Fourth, in some variations, additional or alternative to those describedabove, the system and/or method confers the benefit of partially orfully automating one or more processes related to collision detectionand/or reconstruction. In preferred variations, for instance, the methodis partially or fully implemented with artificial intelligence (e.g.,machine learning and/or deep learning models, with machine learningand/or deep learning modules, etc.). Additionally, the system and/ormethod can function to continue to learn (e.g., be continuouslyretrained) from the continuously detected telematic data collected fromdrivers. In specific examples, the model used to process telematicinformation (and optionally other information), implements bothtraditional machine learning and deep learning processing, whichimproves performance of the model in automatically producing one or moreparameters associated with the collision. In specific examples, thesystem and/or method confers the benefit of automatically initiating,performing, and/or verifying an insurance claim in response to acollision.

Additionally or alternatively, the system and method can confer anyother suitable benefit(s).

3. System 100

A system 100 for accident reconstruction can include and/or beconfigured to interface with any or all of: a set of models, a set ofmodules, a processing system, client application, a user device(equivalently referred to herein as a mobile device), a set of sensors,a vehicle, and/or any other suitable components.

The system can additionally or alternatively include any or all of thesystems, components, embodiments, and examples described in any or allof: U.S. application Ser. No. 15/243,565, filed 22 Aug. 2016; U.S.application Ser. No. 14/566,408, filed 10 Dec. 2014; U.S. applicationSer. No. 14/206,721, filed 12 Mar. 2014; and U.S. application Ser. No.17/111,299, filed 3 Dec. 2020, each of which is incorporated herein inits entirety by this reference.

The system 100 preferably includes a set of one or more models no,wherein the set of models includes a collision model, wherein thecollision model functions to assess (e.g., detect, classify, etc.) theoccurrence of a collision (e.g., a suspected collision based on a set ofinputs as described in the method below), and to optionally assess(e.g., detect, check for, determine a likelihood of, assign aprobability to, etc.) one or more features associated with thecollision. The collision model and/or any other models can additionallyor alternatively function to determine a set of probabilistic outputsrelated to a collision and/or features of a collision, organize and/orlabel data from the set of inputs (e.g., to identify the most relevantdata for further analysis), determine a set of parameters associatedwith a set of inputs (e.g., speed of vehicle just before collision,acceleration of vehicle before and/or during and/or after a collision,GPS coordinates associated with the vehicle before and/or during and/orafter a collision, etc.), initiate one or more actions (e.g.,notifications, emergency responses, etc.) in response to the modeloutputs, and/or can perform any other suitable functions.

The set of features assessed can include, but is not limited to, any orall of: a severity of the collision (e.g., relative to the driver,relative to the vehicle, relative to other vehicles involved, etc.), adirection of impact (e.g., frontal impact, rear impact, broad side, sideswipe, rollover, etc.) of the vehicle involved in the collision, alocation of impact (e.g., bumper, fender, passenger side door, driverside door, rear windshield, etc.), airbag deployment, the presenceand/or absence of passengers (e.g., and/or number of passengers) withinthe vehicle, and/or any other suitable features (e.g., a number ofvehicles involved in the collision). Additionally or alternatively, anyor all of these features (and/or any other features) can be assessed inthe set of modules described below, and/or with any other components ofthe system. In preferred variations, the collision model functions toproduce outputs (e.g., probabilistic outputs, classifications, etc.)related to: whether or not a collision has occurred, a severityassociated with the collision (e.g., “severe” vs. “not severe”,“fatality” vs. “no fatality”, a type of severity, etc.), and a directionof impact (e.g., frontal, rear, broad side, side swipe, rollover)(equivalently referred to herein as a type of impact). Additionally oralternatively, any other features can be investigated.

The collision model and/or any other suitable models of a set of modelspreferably includes one or more algorithms (e.g., machine learningalgorithms, deep learning algorithms, etc.), wherein any or all of thealgorithms further preferably implement one or more machine learningprocesses (e.g., traditional “non-deep-learning” machine learning, deeplearning, etc.).

In preferred variations, the machine learning processes include bothtraditional machine learning and deep learning processes, whereintraditional machine learning refers to models absent of the many hiddenlayers of the deep learning processes such as deep neural networks. Thiscombination can function, for instance, to improve performance of theclassification with respect to any or all of: accuracy of theclassifications, computing resources and/or time required to determinethe classifications, and/or can otherwise improve performance and/orconfer any other advantages. In specific examples, the traditionalmachine learning processes implement zero hidden layers, but canadditionally or alternatively implement non-zero hidden layers, such asa number of hidden layers less than a threshold number (e.g., 2, 3, 4,5, 10, 20, between 2 and 10, between 10 and 20, greater than 20, etc.)and/or be otherwise defined. The deep learning is preferably implementedwith one or more neural networks (e.g., deep neural networks [DNNs],convolutional neural networks [CNNs], recursive neural networks [RNNs],reinforcement learning [RL], inverse reinforcement learning [IRL],etc.), but can additionally or alternatively be implemented with anyother deep learning architectures.

In additional or alternative variations, the machine learning processescan include only traditional machine learning, only deep learning,and/or any other types of artificial intelligence.

In further additional or alternative variations, any or all of themodels can implement non-machine-learning processes, such as classicalprocesses (e.g., rule-based algorithms, dynamic equations to determineone or more motion characteristics of the vehicle, etc.).

In yet further additional or alternative variations, any or all of themodels can include and/or interface with any or all of the systems,components, embodiments, and/or examples described in U.S. applicationSer. No. 15/243,565, filed 22 Aug. 2016, which is incorporated herein inits entirety by this reference. In specific examples, for instance, thecollision detection algorithm of the collision model can be implementedaccording to the embodiments described in this Application. Additionallyor alternatively, any or all of the processes in this Application can beimplemented prior to the model 110, such as to initially detect asuspected collision (and/or identify the data related to the suspectedcollision), which is further processed in the collision model no.

The set of algorithms of the model(s) preferably function to classifythe set of inputs relative to any or all of the categories (e.g.,collision vs. no collision, collision features, etc.) described above.As such, the set of algorithms preferably includes one or moreclassification algorithms (equivalently referred to herein asclassifiers), wherein the algorithms are configured to determineclassifications for the set of inputs. The classifications (outputs ofthe classification algorithms) can be in the form of any or all of:probabilities (e.g., relative to a set of binary classes, relative to aset of non-binary classes, as shown in FIG. 3, etc.), labels, labeleddata, and/or any other formats. In a preferred set of variations, theclassification algorithms implement gradient boosting, such as through aset of gradient boosting machines. Additionally or alternatively, themodel(s) can implement any other classification algorithms, such as butnot limited to, any or all of: k-nearest neighbor, case-based reasoning,decision tree, naive Bayes, artificial neural networks, and/or any otheralgorithms. In specific examples, the algorithms implement bothtraditional machine learning and deep learning processes, wherein thetraditional machine learning techniques include one or more gradientboosting machines.

Additionally or alternatively, classification(s) can be performed withone or more classical (e.g., classically programmed, rule-based, etc.)approaches.

Further additionally or alternatively, the set of algorithms can includeany other suitable algorithms configured for classification or purposesother than classification, such as any or all of: equations, decisiontrees, lookup tables, and/or any other suitable components or processes.In some variations, for instance, any or all of the models 110 functionto determine auxiliary information related to driver and/or the vehicle,such as, but not limited to, any or all of: motion parameters of thevehicle such as a speed (e.g., speed, speed just before the collision,speed during collision, speed just after collision, average speed duringtrip, etc.), acceleration (e.g., G-force associated with the vehicle),distance (e.g., braking distance), etc.; historical information (e.g.,driver's driving history, number of collisions driver has been involvedin, driving style and/or behaviors of driver, etc.); driver information(e.g., risk score assigned to driver); circumstantial/environmentalinformation (e.g., time of day at which collision occurred, region inwhich collision occurred such as school zone vs. residential zone vs.highway, traffic conditions at time of collision, weather conditions attime of collision, road features and/or landmarks such as potholes,etc.); and/or any other suitable auxiliary information.

In a first set of variations, the collision model no includes a set ofclassifiers configured to determine a set of probabilistic outputsrelated to the collision and optionally one or more features of thecollision. In specific examples, the set of classifiers includes acollision detection classifier configured to determine a probabilitythat a collision has occurred, a probability that the collision issevere, and a probability associated with a direction of impact of thevehicle (e.g., probability associated with each possible direction ofimpact, direction of impact associated with the highest probability,etc.).

In a second set of variations, the collision model 110 includes a set ofclassifiers configured to determine a set of class labels related to thecollision and optionally one or more features of the collision. Inspecific examples, the set of classifiers includes a collision detectionclassifier configured to determine a yes/no determination that acollision has occurred, a label indicating whether the collision wassevere or not severe, and a label identifying the particular directionor directions of impact on the vehicle.

The system preferably includes a set of reconstruction modules 120,wherein the set of reconstruction modules functions to produce outputsfor use by various different entities in reconstructing and/orunderstanding various potential aspects of the collision. These entitiespreferably include one or more insurance entities (e.g., insurancecompany associated with the driver and/or other passengers, insurancecompany associated with the driver of any vehicle involved in thecollision, insurance agent, etc.), but can additionally or alternativelyinclude the driver, individuals associated with the driver (e.g., familymembers), and/or any other suitable entities.

Additionally or alternatively, the reconstruction modules can functionto create and/or autofill a portion or all of one or more documents(e.g., insurance claims, accident reports, etc.), facilitatecommunication to and/or between entities (e.g., automatically notify aninsurance entity of the collision such as through a first notice of loss[FNOL]), communicate with the driver to confirm a collision and/or offerassistance, automatically call 911 for assistance, etc.), trigger one ormore response actions (e.g., as described above), perform any or all ofthe functions described above for the models 110, and/or can perform anyother suitable functions.

The set of modules preferably includes multiple modules, wherein each ofthe multiple modules functions to reconstruct a portion (e.g., afeature) or all of the collision. In some variations, for instance, theset of modules includes modules configured to detect any or all of: aconfidence associated with the occurrence of a collision, a severity ofthe collision, a direction of impact of the vehicle, fraud involved inthe collision, and/or the set of modules can be configured to detect anyother suitable information related to the collision.

The modules can include any or all of: machine learning models and/oralgorithms (e.g., traditional machine learning models, deep learningmodels, any or all of those described above, etc.); classical models(e.g., rule-based algorithms, decision trees, lookup tables, mappings,etc.); other models; and/or any combination of models. For modulesincluding machine learning approaches, the modules preferably includeregression models, but can additionally or alternatively includeclassifiers and/or any other suitable models.

In a first set of variations, the set of modules includes a 1^(st)subset and a 2^(nd) subset, wherein the 1^(st) subset includes machinelearning regression models and wherein the second subset includesrule-based algorithms. In specific examples, the 1^(st) subset ofmodules includes any or all of: a confidence detection module, adirection of impact module, and a severity detection module, and whereinthe 2^(nd) subset includes a fraud detection module.

Additionally or alternatively, the set of modules can include onlymachine learning regression models, only rule-based algorithms, othermodels and/or algorithms, and/or any combination.

The system can include and/or interface with a processing system, whichfunctions to receive and process any or all of the information (e.g.,the set of inputs described below) associated with the accident(equivalently referred to herein as a collision). In some variations,for instance, the processing system is involved in any or all of:receiving a movement dataset collected during a time period of vehiclemovement; extracting a set of movement features associated with at leastone of a position, a velocity, and an acceleration characterizing themovement of the vehicle during the time period; detecting a vehicularaccident event from processing the set of movement features with anaccident detection model; determining an accident characteristic (e.g.,severity); reconstructing the accident; and producing an outputassociated with the reconstruction. Additionally or alternatively, theprocessing system can perform any other suitable functions.

The processing system can be any or all of: associated with a singlecomponent, distributed among multiple components (e.g., mobile devices,mobile device and remote processing system, etc.), and/or anycombination of these. At least a portion of the processing system ispreferably arranged remotely (e.g., at a remote computing system and/ora cloud-based computing system), but can additionally or alternativelybe arranged locally (e.g., onboard the vehicle), onboard one or moredevices (e.g., mobile user devices), any combination, and/or otherwisearranged. In a first set of variations, for instance, the processingsystem is arranged remotely at a remote computing system, wherein theremote computing system is in communication with a user deviceassociated with the user (e.g., the driver), wherein the remotecomputing system receives information from the user's user device (e.g.,through an SDK operating on the user device). Additionally oralternatively, the processing system can include and/or interface withany other processing systems, such as those mounted to and/or within thevehicle, those of a vehicle's on-board diagnostics (OBD) system, and/orany other suitable processing systems.

Database(s) and/or portions of the method 200 can be entirely orpartially executed, run, hosted, or otherwise performed by: a mobilecomputing device, a remote computing system (e.g., a server, at leastone networked computing system, stateless computing system, statefulcomputing system, etc.), a machine configured to receive acomputer-readable medium storing computer-readable instructions, or byany other suitable computing system possessing any suitable component(e.g., a graphics processing unit, a communications module, etc.).Mobile computing devices implementing at least a portion of the method200 can include one or more of: a smartphone, a wearable computingdevice (e.g., head-mounted wearable computing device, a smart watch,smart glasses), tablet, desktop, a supplemental biosignal detector, asupplemental sensor (e.g., motion sensors, magnetometers, audio sensors,video sensors, location sensors a motion sensor, a light sensor, etc.),a medical device, and/or any other suitable device. All or portions ofthe method 200 can be performed by one or more of: a native application,web application, firmware on the device, plug-in, and any other suitablesoftware executing on a device. Device components used with the method200 can include an input (e.g., keyboard, touchscreen, etc.), an output(e.g., a display), a processor, a transceiver, and/or any other suitablecomponent, where data from the input device(s) and/or output device(s)can be generated, analyzed, and/or transmitted to entities forconsumption (e.g., for a user to assess their health parameters)Communication between devices and/or databases can include wirelesscommunication (e.g., WiFi, Bluetooth, radiofrequency, etc.) and/or wiredcommunication.

However, the components of the system can be distributed across machineand cloud-based computing systems in any other suitable manner.

The system is preferably configured to facilitate reception andprocessing of the data and information described below, but canadditionally or alternatively be configured to receive and/or processany other suitable type of data. As such, the processing system can beimplemented on one or more computing systems including one or more of: acloud-based computing system (e.g., Amazon EC3), a mainframe computingsystem, a grid-computing system, and any other suitable computingsystem. Furthermore, reception of data by the processing system 205 canoccur over a wired connection and/or wirelessly (e.g., over theInternet, directly from a natively application executing on anelectronic device of the patient, indirectly from a remote databasereceiving data from a mobile computing device, etc.).

However, one or more mobile computing devices, vehicles, remote servers,and/or other suitable computing systems can be communicably connected(e.g., wired, wirelessly) through any suitable communication networks.For example, a non-generalized mobile computing device (e.g., smartphoneincluding at least one of a location sensor and motion sensor, etc.) canbe configured for any or all of: to collect a movement dataset, toreceive additional movement datasets from the vehicle (e.g., OBD port)and/or other mobile computing devices (e.g., a wearable fitness trackercoupled to the driver's wrist), and to detect a vehicular accident eventwith the collected data, where the mobile computing device, the vehicle,and the other mobile computing devices are communicably connectedthrough one or more wireless links (e.g., Bluetooth). In anotherexample, a remote server can be configured to receive movement data froma vehicle and a mobile computing device, to detect a vehicular accidentevent based on the received data, and to automatically contact emergencyservices (e.g., through a telecommunications API), where the a remoteserver, vehicle, and mobile computing device are connected over awireless network. However, the system can include any suitableconfiguration of non-generalized computing systems connected in anycombination to one or more communication networks.

Components of the system and/or any suitable portion of the method 200can employ machine learning approaches including any one or more of:supervised learning (e.g., using logistic regression, using backpropagation neural networks, using random forests, decision trees,etc.), unsupervised learning (e.g., using an Apriori algorithm, usingK-means clustering), semi-supervised learning, reinforcement learning(e.g., using a Q-learning algorithm, using temporal differencelearning), any or all of the approaches described above, and any othersuitable learning style. Each model and/or module can implement any oneor more of: a regression algorithm (e.g., ordinary least squares,logistic regression, stepwise regression, multivariate adaptiveregression splines, locally estimated scatterplot smoothing, etc.), aninstance-based method (e.g., k-nearest neighbor, learning vectorquantization, self-organizing map, etc.), a regularization method (e.g.,ridge regression, least absolute shrinkage and selection operator,elastic net, etc.), a decision tree learning method (e.g.,classification and regression tree, iterative dichotomiser 3, C4.5,chi-squared automatic interaction detection, decision stump, randomforest, multivariate adaptive regression splines, gradient boostingmachines, etc.), a Bayesian method (e.g., naïve Bayes, averagedone-dependence estimators, Bayesian belief network, etc.), a kernelmethod (e.g., a support vector machine, a radial basis function, alinear discriminate analysis, etc.), a clustering method (e.g., k-meansclustering, expectation maximization, etc.), an associated rule learningalgorithm (e.g., an Apriori algorithm, an Eclat algorithm, etc.), anartificial neural network model (e.g., a Perceptron method, aback-propagation method, a Hopfield network method, a self-organizingmap method, a learning vector quantization method, etc.), a deeplearning algorithm (e.g., a restricted Boltzmann machine, a deep beliefnetwork method, a convolution network method, a stacked auto-encodermethod, etc.), a dimensionality reduction method (e.g., principalcomponent analysis, partial lest squares regression, Sammon mapping,multidimensional scaling, projection pursuit, etc.), an ensemble method(e.g., boosting, bootstrapped aggregation, AdaBoost, stackedgeneralization, gradient boosting machine method, random forest method,etc.), and any suitable form of machine learning algorithm. Eachprocessing portion of the method 200 can additionally or alternativelyleverage: a probabilistic module, heuristic module, deterministicmodule, or any other suitable module leveraging any other suitablecomputation method, machine learning method or combination thereof.

The method 200 and/or system 100 of the embodiments can be embodiedand/or implemented at least in part as a machine configured to receive acomputer-readable medium storing computer-readable instructions. Theinstructions can be executed by computer-executable componentsintegrated with the application, applet, host, server, network, website,communication service, communication interface,hardware/firmware/software elements of a patient computer or mobiledevice, or any suitable combination thereof. Other systems and methodsof the embodiments can be embodied and/or implemented at least in partas a machine configured to receive a computer-readable medium storingcomputer-readable instructions. The instructions can be executed bycomputer-executable components integrated with apparatuses and networksof the type described above. The computer-readable medium can be storedon any suitable computer readable media such as RAMs, ROMs, flashmemory, EEPROMs, optical devices (CD or DVD), hard drives, floppydrives, or any suitable device. The computer-executable component can bea processor, though any suitable dedicated hardware device can(alternatively or additionally) execute the instructions.

In a first variation of the system 100 for collision detection and/orreconstruction, the system 100 includes a set of collision models 110and a set of reconstruction modules 120, wherein the set of collisionmodels no and the set of reconstruction modules are implemented at aprocessing system arranged at least partially remotely, wherein theprocessing system is configured to receive telematic information (e.g.,from a mobile device of the driver) with which to at least partiallyevaluate the models and/or modules.

Additionally or alternatively, the system 100 can include any othersuitable components configured to perform any suitable part(s) of themethod 200.

4. Method 200

As shown in FIG. 1, the method 200 for accident reconstruction includescollecting a set of inputs S205; detecting a collision and/or one ormore features of the collision S210; reconstructing the collision S220;and producing an output based on the reconstruction S230. Additionallyor alternatively, the method 200 can include training a set of modelsand/or modules, and/or any other suitable processes.

Further additionally or alternatively, the method 200 can include anyother suitable processes and/or any or all of the methods, processes,embodiments, and examples described in any or all of: U.S. applicationSer. No. 15/243,565, filed 22 Aug. 2016; and U.S. application Ser. No.14/566,408, filed 10 Dec. 2014; U.S. application Ser. No. 15/702,601,filed 12 Sep. 2017; U.S. application Ser. No. 15/835,284, filed 7 Dec.2017; U.S. application Ser. No. 15/401,761, filed 9 Jan. 2017; U.S.application Ser. No. 16/022,120, filed 28 Jun. 2018; U.S. applicationSer. No. 16/022,184, filed 28 Jun. 2018; U.S. application Ser. No.16/166,895, filed 22 Oct. 2018; U.S. application Ser. No. 16/201,955,filed 27 Nov. 2018; U.S. application Ser. No. 17/700,991, filed 2 Dec.2019; U.S. application Ser. No. 14/206,721, filed 12 Mar. 2014; and U.S.application Ser. No. 17/111,299, filed 3 Dec. 2020, each of which ishereby incorporated in its entirety by this reference.

The method 200 is preferably performed with a system 100 as describedabove, but can additionally or alternatively be performed with any othersuitable system(s).

The method 200 preferably functions to automatically detect andreconstruct a collision, but can additionally or alternatively functionto produce one or more outputs in light of a collision,

4.1 Method: Collecting a Set of Inputs S205

The method 200 includes collecting a set of inputs S205, which functionsto collect information (e.g., data) with which to determine theoccurrence of a collision and to reconstruct on or more features of thecollision.

The set of inputs collected in S205 is preferably at least partiallyreceived from a set of one or more mobile devices, further preferablyfrom one or more sensors of the mobile device, such as through asoftware development kit (SDK) and/or a client application executing oneach of the set of mobile devices. Additionally or alternatively,information can be collected from the vehicle (e.g., through one or moresensors of the vehicle, through an on-board diagnostics port, etc.),devices external to a vehicle (e.g., road infrastructure devices such ascameras associated with a set of traffic lights), one or more users(e.g., observers), and/or any other suitable information sources.

The information is preferably, at least in part, collected continuouslywhile the vehicle is being driven and optionally while the vehicle isnot being driven (e.g., while parked). Additionally or alternatively,any or all of the information can be collected periodically (e.g., atregular intervals, at irregular intervals, at a predetermined frequency,etc.). The information is further preferably collected automatically inresponse to a trigger (e.g., detection of a nonzero vehicle speed toindicate the initiation of a trip), but can additionally oralternatively be collected in response to multiple triggers, collectedin response to a user input (e.g., notification from a user that anaccident has occurred) and/or permission, and/or otherwise collected.

S205 is preferably performed continuously throughout the method 200 andfurther continuously while the driver is driving, but can additionallyor alternatively be performed once, multiple times, and/or anycombination. S205 is preferably performed prior to S210 but canadditionally or alternatively be performed prior to S220, after S210,prior to S230, after S230, and/or at any other suitable times. Furtheradditionally or alternatively, an instance of S205 can be performedprior to S210 (e.g., wherein a collision is detected based on a firstset of inputs) and after S210, wherein the second set of inputs and/orany or all of the first set of inputs is used to reconstruct thecollision.

The information collected in S205 is preferably received at a processingsystem (e.g., remote computing system, mobile device computing system,processor of mobile device, etc.) and/or database of the system (e.g.,as described above) for processing and/or storage, but can additionallyor alternatively be received at any suitable location(s).

In the event of a collision, at least a portion of the information ispreferably collected contemporaneously with the collision, such asduring the collision, just prior to the accident, and/or immediatelyfollowing the collision. In variations in which information is collectedfollowing a collision, the method 200 can optionally include overridingone or more triggers (e.g., vehicle speed below a predeterminedthreshold, vehicle reaching an ‘off’ state, detection of a vehiclemalfunction, etc.) for ceasing data collection. In specific examples,for instance, a trigger normally associated with the end of a vehicle'sdrive (e.g., vehicle has stopped moving, vehicle has zero speed for apredetermined duration, etc.) is overridden in response to detecting anaccident (such as through any or all of the processes described in U.S.application Ser. No. 15/243,565, filed 22 Aug. 2016, which isincorporated in its entirety by this reference), the collection ofinformation continues.

S205 can optionally include any or all of: marking, tagging, flagging,prioritizing, ranking, filtering (e.g., signals filtering, filtering todifferent locations, etc.), pre-processing, or otherwise adjusting theinformation collected in S210. In some variations, for instance,information collected contemporaneously with an accident is tagged forfurther (e.g., more detailed) processing.

The information collected in S205 preferably includes locationinformation associated with the location and/or trajectory of thevehicle. This can include any or all of: geographical information (e.g.,GPS coordinates), route information (e.g., vehicle is located at anintersection, vehicle is in an outside lane, vehicle is at a stop sign,etc.), a starting location of the vehicle's route, a destination of thevehicle (e.g., as an input into a navigation application, etc.), and/orany other suitable information. The location information is preferablycollected from a GPS sensor of a mobile device of an individual insidethe vehicle via an SDK operating on the mobile device, but canadditionally or alternatively be otherwise received from any suitablesource.

The information collected in S205 can additionally or alternativelyinclude temporal information associated with the vehicle, such as any orall of: a time (e.g., associated with the occurrence of an accident),one or more temporal characteristics associated with (e.g., determinedbased on, derived from, etc.) a time (e.g., accident occurred duringrush hour, accident occurred outside of rush hour, traffic level at theassociated time, predicted activity of the user, etc.), and/or any othersuitable information.

Further additionally or alternatively, the information collected in S205can include vehicle information associated with one or more vehicles.The one or more vehicles preferably includes a vehicle involved in theaccident, but can additionally or alternatively include a vehicle nearbythe accident (e.g., associated with an observer), a vehicle unrelated tothe accident (e.g., associated with the route, aggregated into a set ofdata, etc.), and/or any other suitable vehicles.

The vehicle information preferably includes movement information(equivalently referred to herein as motion information) associated withthe vehicle, which refers to any data related to the position, velocity,and/or acceleration of any or all of the vehicles described above. Inpreferred variations, the motion information collected and/or determinedbased on information collected in S205 includes acceleration, velocity,and position information, but can additionally or alternatively includeother motion information and/or a subset of this information (e.g., onlyacceleration).

Movement data can be collected from and/or associated with any one ormore of: motion sensors (e.g., multi-axis and/or single-axisaccelerometers, gyroscopes, etc.), location sensors (e.g., GPS datacollection components, magnetometer, compass, altimeter, etc.), and/orany other suitable components. Such components are preferably arrangedat a mobile computing device (e.g., smartphone, laptop, tablet, smartwatch, smart glasses, medical devices, etc.) associated with a user(e.g., a vehicle driver), wherein the motion information of the mobilecomputing device is used to determine (e.g., equated to, based onorientation information of the mobile computing device, etc.) motioninformation corresponding to the vehicle in which the mobile computingdevice is located. As such, the movement data can optionally includeorientation information from the mobile computing device (e.g., ineast-north-up coordinates), wherein the orientation information is usedto isolate and remove (e.g., subtract) movement of the mobile computingdevice relative to the vehicle, thereby enable movement of the vehiclerelative to the earth to be determined and/or better approximated.

In some variations, for instance, S205 can include receiving one or moremovement datasets collected at least at one of a location sensor and amotion sensor arranged within the vehicle, where the location sensor andthe motion sensor are arranged at a mobile computing device positionedwithin the vehicle. Additionally or alternatively, the vehicle caninclude onboard sensors (e.g., speedometers, accelerometers, etc.) usedin collecting movement data. For example, S205 can include collecting amovement dataset at at least one of a location sensor and a motionsensor of a vehicle (e.g., a vehicle that is being driven by the user).However, any suitable components associated with any suitable devicescan be used in collecting movement data and/or data from which movementdata can be derived. For example, S205 can include deriving movementdata from visual data (e.g., deriving velocity from video taken by asmartphone's camera) and/or audio data (e.g., deriving motion fromDoppler effects captured in data from the smartphone's microphone,etc.), as described in more detail below.

The vehicle information can additionally or alternatively include datafrom vehicle sensors such as OEM sensors and/or sensors mounted toand/or otherwise coupled to the vehicle, which can include any one ormore of: proximity sensor data (e.g., radar, electromagnetic sensordata, ultrasonic sensor data, light detection and ranging, lightamplification for detection and ranging, line laser scanner, laserdetection and ranging, airborne laser swath mapping, laser altimetry,sonar, etc.), vehicle camera data (e.g., in-car cameras, exteriorcameras, back-up cameras, dashboard cameras, front-view cameras,side-view cameras, image recognition data, infrared camera, 3D stereocamera, monocular camera, etc.), car speed, RPM data, odometer,altimeter, location sensor data (e.g., GPS data, compass data, etc.),motion sensor data (e.g., from an accelerometer, gyroscope, etc.),environmental data (e.g., pressure, temperature, etc.), light sensordata (e.g., from infrared sensors, ambient light sensors, etc.), fuellevel (e.g., percentile-based, distance-based, low warning, etc.), fuelsystem status, oxygen sensor data, throttle position, gear data (e.g.,drive, neutral, park, reverse, gear number, etc.), HVAC data (e.g.,current temperature, target temperature, etc.), driving status (e.g.,restricted features such as user inputs into control panel, unrestrictedfeatures, etc.), and/or any other suitable vehicle data. For example,vehicle sensor data can provide information on the status of a vehiclebefore/during/after an accident (e.g., by collecting airbag deploymentdata, ABS or other braking system data, engine temperature data, etc.).For example, S205 can include receiving a proximity dataset collected ata proximity sensor of the vehicle.

Vehicle sensor data (e.g., vehicle subsystem status data) can optionallypartially or fully be collected via the vehicle's on-board diagnostics(OBD) port. For example, S205 can include using OBD-II Parameter IDs torequest OBD data from a vehicle. In another example, S205 can includedetecting deployment of an airbag through a communicable link with avehicle. However, collecting vehicle sensor data through the OBD systemcan be performed in any manner and/or the S205 can be performed inabsence of collecting information from an OBD port and/or vehicle sensordata can be collected in any other suitable ways.

In a first variation, the vehicle information collected in S205 and/ordetermined based on vehicle information collected in S205 includes anyor all of: a vehicle location (e.g., in GPS coordinates), a vehiclespeed (e.g., deceleration speed), a vehicle acceleration, a vehicleaction (e.g., braking, accelerating, turning, maintaining a speed,etc.), a vehicle heading (e.g., forward, backward, particular headingangle, etc.), a type of vehicle (e.g., make of vehicle involved inaccident), an airbag deployment parameter (e.g., as measured as a changein pressure in the vehicle cabin), a G-force/severity of an accident, adirection and/or location of impact, a stopping distance of the vehicle,and/or any other suitable information.

In specific examples, the information collected in one or more instancesof S205 includes information which occurs in time after the collision(e.g., user's location after collision, etc.), which can be used, forinstance, in evaluating one or more models and/or modules (e.g., asdescribed below). In a particular specific example, for instance, theuser's location after a collision is used to see where the user travelsto (e.g., to see if user goes to an auto body shop, hospital, home,stays in middle of highway, etc.), which can function to determine anyor all of: whether or not a collision occurred (e.g., at the collisiondetection algorithm, at the confidence module, etc.), whether or not thecollision was severe (e.g., at the severity detection algorithm),whether or not fraud was detected (e.g., at the fraud module), and/orany suitable outputs.

The information collected in S205 can additionally include driverinformation, such as any or all of: historical information associatedwith the driver (e.g., prior history of collisions, prior history offraud, riskiness of driver's driving behavior as represented in adriving risk score, etc.), a driver's attention and/or distractionlevel, an activity of the driver (e.g., texting), a driver score (e.g.,safety score, risk score, etc.), a driver behavior (e.g., duringaccident, prior to accident, after accident, etc.), circumstantialinformation (e.g., driver had a long work day beforehand and was tired,driver was in an area he had never driven in before, etc.), intoxicationlevel, and/or any other suitable information. Further additionally oralternatively, any other user information, such as passengerinformation, observer information, and/or any other information can becollected.

The information collected in S205 can further additionally oralternatively include environmental and/or circumstantial information(e.g., any or all of the auxiliary information described above) whichcharacterize a driving environment of the vehicle, such as any or allof: a type of road (e.g., highway, residential, school-zone road,hospital-zone road, parking lot, one-way road, two-way road, number oflanes of road, etc.), environmental conditions at the time of driving(e.g., weather conditions, road conditions, time of day/amount of light,etc.), road conditions (e.g., paved surface, un-paved surface, smoothsurface, pothole incidence, etc.), traffic conditions (e.g., trafficlevel), and/or any other suitable information.

The information collected in S205 can further additionally oralternatively include information directly related to a collision, suchas any or all of: police reports, claims reports and/or any otherinformation from an insurance entity, information related messagesand/or calls and/or notifications at a mobile computing device of theuser (e.g., indicating that user has called emergency services,indicated that user has called an auto body shop, indicating that useris responsive, etc.), and/or any other suitable information.

In a first variation of S205, all of the information collected iscollected via an SDK of a set of one or more mobile devices, wherein atleast one mobile device is associated with a vehicle involved in theaccident.

In a second variation of S205, a first portion of the set of inputs iscollected is collected via an SDK of a set of one or more mobiledevices, wherein at least one mobile device is associated with a vehicleinvolved in the accident, and a second portion of the set of inputs iscollected from any or all of: a processing system onboard and/or mountedto the vehicle, one or more stored databases (e.g., at the mobiledevice, at remote storage, etc.), an online source (e.g., weathersource, traffic conditions source, etc.), and/or from any other suitablesources.

Additionally or alternatively, the set of inputs collected in S205 caninclude any other suitable inputs collected in any suitable ways fromany suitable sources.

4.2 Method: Detecting a Collision and/or One or More Features of theCollision S210

The method preferably includes detecting a collision and/or one or morefeatures of the collision S210, which functions to classify the set ofinputs in S205 as corresponding to a collision (or not). Additionally oralternatively, S210 can function to determine (e.g., detect, classify,etc.) one or more features associated with a collision (e.g., severity,direction of impact, etc.), produce a set of outputs with which toperform S220, trigger an response (e.g., an emergency response), and/orcan perform any other suitable functions.

S210 is preferably performed at a processing system (e.g., remoteprocessor, processor of one or more mobile devices, etc.) and/orcomputing system (e.g., remote computing system), such as any of thosedescribed above in the system 100, but can additionally or alternativelybe performed by any suitable component(s).

S210 is preferably performed in response to S205, but any or all of S210(e.g., the collision detection algorithm) can additionally oralternatively be performed prior to S205 (e.g., to prompt collection ofone or more inputs in S205) and/or at any other suitable times.Additionally or alternatively, S210 can be performed in parallel withand/or part of S220. Further additionally or alternatively, S210 can beperformed multiple times throughout the method 200 (e.g., continuously,at a predetermined frequency, at a set of random intervals, etc.) and/orat any other suitable times.

S210 includes analyzing (e.g., assessing, processing, etc.) theinformation collected in S205, with a set of models 110 including acollision model (e.g., as shown in FIG. 1). The collision modelpreferably includes a set of one or more classifiers (e.g., as describedabove), further preferably a set of machine learning (e.g., traditionalmachine learning, deep learning, etc.) classifiers, such as any or allof the classifiers described above. Additionally or alternatively, anyother suitable algorithms and/or models can be used in the collisionmodel.

The collision model preferably receives as input at least a set ofsensor inputs received in S205 (e.g., from a mobile device of thedriver), such as any or all of: location information (e.g., GPScoordinates from a GPS sensor of the mobile device), accelerationinformation (e.g., from an accelerometer of the mobile device, from aninertial measurement unit [IMU] of the mobile device), pressureinformation (e.g., from a barometer of the mobile device to detectchanges in pressure in the vehicle as resulting from airbag deployment,for instance), orientation information (e.g., from an IMU of the mobiledevice), and/or any other suitable sensor information. Additionally oralternatively, the collision model can receive sensor information fromsensors independent of (e.g., offboard) the mobile device (e.g., fromvehicle sensors, from environmental sensors, etc.), information otherthan sensor information (e.g., historical information, a risk scoreand/or prior driving behavior of the driver, environmental information,etc.), and/or any other suitable information.

The algorithms (e.g., classifiers) of the collision model canindividually and/or collectively function to take into account andaggregate the multiple types of information sources collected above,which as previously described, are conventionally not available forreconstruction analyses. In examples, the algorithms of the collisionmodel can function to take into account circumstantial driverinformation (e.g., duration of drive, driver risk score, driver's amountof phone use, driver distraction level, etc.), along with locationand/or vehicle information (e.g., speed, direction, location, etc.) todetect a collision and/or determine one or more features of thecollision.

The outputs of the algorithms can include and/or be used to determineany or all of: a binary output (e.g., binary classification, binaryoutput determined based on a set of probabilities, etc.), a non-binaryoutput (e.g., a non-binary classification, a multi-class classification,etc.), and/or any combination of outputs. Further additionally oralternatively, the algorithm outputs (or other outputs of the collisionmodel) can include any or all of: parameter values (e.g., vehicle speed,vehicle acceleration, outputs from one or more dynamic equations, motionparameter values, etc.) such as those related to auxiliary informationas described above and/or below, a set of parameters (e.g., weights) foruse in implementing one or more modules in S220, and/or any othersuitable outputs.

In a preferred set of variations, the algorithms in the collision modelproduce one or more probabilistic outputs, which can be used to make aset of one or more classifications (e.g., based on comparison with oneor more probability thresholds, based on comparison with each otherwherein the class/label associated with the higher probability isselected, as shown in FIG. 3, etc.), trigger S220 (e.g., in response todetermining a collision has occurred), and/or be used as inputs in S220(e.g., wherein any or all of the modules determine outputs based in partor in full on the probabilistic outputs). Additionally or alternatively,the outputs can be used, for instance, in triggering a response and/orfor use in producing an output in S230 (e.g., a report, an insuranceclaim, etc.). The collision model further preferably calculates and/orpasses along auxiliary information which is used by the set of modulesto determine any or all of the module outputs. Additionally oralternatively, auxiliary information can be produced by other model(s),S210 can be absent of producing and/or interfacing with auxiliaryinformation, and/or the model in S210 can be otherwise evaluated.

The set of algorithms preferably includes a collision detectionalgorithm (e.g., as described above), wherein the collision detectionalgorithm functions to detect that a collision has occurred.Additionally or alternatively, the collision detection algorithm canfunction to prompt the performance of other algorithms, trigger anemergency response, and/or can perform any other suitable functions. Thecollision detection algorithm preferably produces a binary determination(and/or enables a binary determination) of whether or not a collisionhas occurred (“yes” collision vs. “no” collision), such as through thedetermination of one or more probability values related to theoccurrence of a collision (e.g., probability that a collision hasoccurred). In specific examples, for instance, these probability valuescan be used to make the binary determination (e.g., by comparing with athreshold, by selecting the higher of a probability of a collisionhaving occurred and the probability of a collision having not hadoccurred, etc.). Additionally or alternatively, the collision detectionalgorithm can produce any other suitable outputs.

In preferred variations, the collision detection algorithm implementsone or more machine learning algorithms, wherein the machine learningalgorithms have been trained (e.g., as described below) to detect acollision. The machine learning algorithms preferably include bothtraditional machine learning and deep learning (e.g., as describedabove), but can additionally or alternatively include classicalapproaches, only traditional machine learning approaches, only deeplearning approaches, and/or any other suitable approaches and/orcombination of approaches. In specific examples, the collision detectionalgorithm is trained to detect a collision based at least on motioninformation associated with the vehicle (e.g., as collected at a mobiledevice of the driver inside of the vehicle), such as motion informationcorresponding to any or all of: a sudden acceleration of the vehicle, asudden deceleration of the vehicle, a sudden change in direction of thevehicle's movement, a change in the vehicle's orientation, and/or anyother suitable indicators of a collision. Additionally or alternatively,the collision detection algorithm can implement any or all of theprocesses, embodiments, and/or examples described in U.S. applicationSer. No. 15/243,565, filed 22 Aug. 2016, which is incorporated herein inits entirety by this reference.

In a first specific example, the collision detection algorithmimplements a gradient boosting machine algorithm (e.g., traditionalmachine learning gradient boosting machine), optionally in combinationwith a deep learning algorithm, configured to determine a classificationof whether or not a collision has occurred.

The set of algorithms further preferably includes one or more collisionfeature detection algorithms, wherein the collision feature detectionalgorithms are configured to classify one or more features associatedwith the collision, such as, but not limited to, a severity of thecollision (e.g., whether or not the collision is determined to besevere), a direction of impact to the vehicle, and/or any other suitablefeatures. Additionally or alternatively, the collision feature detectionalgorithms can be configured to calculate one or more parametersassociated with the collision, assign a ranking to a collision relativeto other collisions, trigger an action and/or response, and/or canperform any other suitable functions.

The collision feature detection algorithms are preferably performed inresponse to (e.g., after) the collision detection algorithm, but canadditionally or alternatively be performed contemporaneously with (e.g.,in parallel with, partially overlapping with, fully overlapping with,etc.) the collision detection algorithm, prior to the collisiondetection algorithm (e.g., prior to a repeated instance of the collisiondetection algorithm), and/or at any other suitable times.

The set of collision feature detection algorithms can optionally includea severity detection algorithm, wherein the severity detection algorithmis preferably configured to determine a binary classification of whetheror not the collision is severe, wherein a severe collision can refer toa collision, for instance, involving any or all of: an injury (e.g., ofthe driver, of a passenger, of a pedestrian, etc.), a fatality (e.g., ofthe driver, of a passenger, of a pedestrian, etc.), vehicular damage(e.g., totaled vehicle, minor damage, no damage, etc.), infrastructuredamage (e.g., hit traffic sign), other factors, a combination offactors, and/or any other suitable classification of collision severity.Additionally or alternatively, the severity detection algorithm candetect a type of severity (e.g., fatality of driver vs. non-fatality ofdriver, fatality of passenger vs. non-fatality of passenger, totaledvehicle vs. non-totaled vehicle, etc.), a severity indication asindicated by a spectrum of severities, one or more parameter values,and/or any other suitable outputs.

The severity detection algorithm is preferably in the form of a machinelearning algorithm, further preferably a gradient boosting machinealgorithm and optionally a deep learning algorithm (e.g., as describedabove for the collision detection algorithm, different than thatdescribed above for the collision detection algorithm, etc.), but canadditionally or alternatively include any other algorithms.

Additionally or alternatively, the set of collision feature detectionalgorithms can optionally include a direction of impact detectionalgorithm, wherein the direction of impact detection algorithm ispreferably configured to classify the impact experienced by the vehiclein the collision. The direction of impact choices preferably include thefollowing options (e.g., labels, classes, etc.), but can additionally oralternatively include any other suitable options: frontal impact, rearimpact, broad side impact, side swipe impact, and rollover impact.Additionally or alternatively, the direction of impact detectionalgorithm can identify a location of impact on the vehicle (e.g.,passenger door, passenger's side rear door, driver door, driver's siderear door, rear of vehicle, rear bumper, front of vehicle, front bumper,etc.), a direction of impact on another vehicle in the collision, and/orany other suitable parameters.

The output(s) of the direction of impact algorithm can include any orall of: a set of probabilities (e.g., associated with each option fordirection of impact), a determined direction of impact (e.g., determinedbased on the set of probabilities, determined independently of the setof probabilities, associated with a particular confidence value, etc.),and/or any other suitable parameters and/or combination.

The direction of impact detection algorithm is preferably a classifier(e.g., multi-class classifier) in the form of a machine learningalgorithm, further preferably a gradient boosting machine algorithm andoptionally a deep learning algorithm (e.g., as described above for thecollision detection algorithm, different than that described above forthe collision detection algorithm, etc.), but can additionally oralternatively include any other algorithms.

In variations of S210 including multiple algorithms, the algorithms canbe implemented in any or all of: series, parallel, and/or anycombination. Additionally or alternatively, the algorithms can be any orall of: initiated automatically (e.g., in response to S205), initiatedin response to a trigger (e.g., in response to detecting a collision, inresponse to the performance of and/or the outputs of another algorithm,in response to a temporal condition, etc.), initiated as prompted by adecision tree and/or lookup table, otherwise initiated, and/or initiatedbased on any combination of triggers and/or prompts.

Additionally or alternatively, the order in which any or all of thealgorithms is performed can be determined based on a priority (e.g.,predetermined priority, dynamically determined priority, etc.)associated with one or algorithms. In some examples, for instance, oneor more algorithms which have the most time-sensitive outputs (e.g., acollision detection algorithm which prompts other algorithms and/orother processes such as emergency responses) are implemented first. In aparticular example, the collision detection algorithm is implementedfirst, wherein the determination made by the collision detectionalgorithm can be used for any or all of: prompting the performance ofother algorithms (e.g., collision feature algorithms) in S210; promptingthe performance of one or more modules in S220; prompting one or moreactions (e.g., notifying the driver at the driver's mobile device,notifying a passenger at the passenger's mobile device, etc.) and/orresponses (e.g., initiating an emergency response, calling an ambulance,etc.) in S230; and/or prompting any other processes. In specificexamples, for instance, in an event that a collision is detected in acollision detection algorithm of S210, the collision detection featurealgorithms can be prompted in return, and in an event that a collisionis determined not to be detected in an collision detection algorithm ofS210, the method can be terminated and/or otherwise proceed (e.g., toS220, to S230, etc.). Additionally or alternatively, any or all of theseprocesses can be performed independently of the outcomes of otheralgorithms, otherwise dependent on the outcomes of other algorithms,and/or can be otherwise performed.

Further additionally or alternatively, any or all of the algorithms canbe performed: a single time, multiple times (e.g., as shown in FIGS.4A-4B), and/or any combination. In collision detection, for instance,this can function to handle a tradeoff between the time required todetect a collision and the accuracy associated with the detection.Implementing this can function, for instance, to both quickly check fora collision in order to provide a quick response if needed and toutilize additional information which continues to be received (e.g., inresponse to repeated instances of S205) to confirm or deny thisdecision. In some cases, for instance, a driver's behavior after acollision can be used to come to a more accurate conclusion as towhether a collision occurred (e.g., if driver's vehicle stops in themiddle of an intersection, if driver's vehicle is left on the highway,if driver calls a tow truck, if driver visits an auto body shop, ifdriver visits a hospital, etc.) or did not (e.g., if driver quicklycontinues driving along original route). In a preferred set ofvariations, the collision detection algorithm in S210 is performedmultiple times (e.g., as shown in FIGS. 4A-4B), such as any or all of: apredetermined number of times (e.g., 2 times, 3 times, between 2 and 5times, 5 times, between 5 and 10 times, 10 times, greater than 10 times,etc.); continuously (e.g., at a predetermined frequency); until athreshold condition and/or trigger condition is met (e.g., untilconfirmation that a collision has not occurred is determined, inresponse to a driver responding to a notification to confirm that he hasnot been in a collision, etc.); and/or any number of times. In specificexamples, the collision detection algorithm is performed 3 times, firstat 20 seconds after a potential collision is detected, again at 60seconds after the potential collision is detected, and yet again at 120seconds after the potential collision is detected. In alternativeexamples, this timing and number of times can be suitably adjusted.

Additionally or alternatively, the collision detection algorithm can beperformed once, other algorithms can be performed multiple times, and/orthe algorithms can be otherwise performed.

S210 can optionally include aggregating (e.g., combining, summing,averaging, combining according to an algorithm and/or equation and/orlookup table, etc.) any or all of the algorithm outputs. Additionally oralternatively, any or all of the algorithm outputs can remainindependent.

In a first variation of S220, a set of machine learning classifiers areimplemented which receive information collected in S205 and process theinformation to determine a set of probabilistic outputs related to anyor all of: an occurrence of a collision, a severity of the collision, adirection of impact of the vehicle in the collision, and/or any othersuitable outputs.

S210 can additionally or alternatively include any other processes. Insome variations, for instance, S210 (and/or any other processes in themethod) include determining one or more driving events, such as, but notlimited to, any or all of: a hard brake event (e.g., in which the driverbrakes a significant amount in a short distance and/or time, in whichthe driver brakes suddenly, etc.); a sudden acceleration event (e.g., inwhich the vehicle changes direction and/or speed suddenly and/or over ashort distance or time); a mobile device usage event (e.g., indicatingthat the driver was using his or her mobile device while driving, adistraction event, etc.); a speeding event (e.g., indicating that thedriver was speeding, indicating that the driver was speeding by at leasta predetermined threshold, etc.); a turning event (e.g., indicating thatthe vehicle turned suddenly); and/or any other suitable driving events.Identification of any or all of these events can be used inreconstructions (e.g., as shown in FIGS. 9A-9G) of a collision, providedas an output to an insurance entity, and/or otherwise used in S230 orelsewhere in the method. In specific examples, for instance, determiningevents can be performed as described in U.S. application Ser. No.17/111,299, filed 3 Dec. 2020, which is incorporated herein in itsentirety by this reference.

In a first set of specific examples, the collision detection algorithmis performed first, wherein the output of the collision detectionalgorithm triggers the performance of the collision feature detectionalgorithms.

In a second set of specific examples, the collision detection algorithmand the collision feature detection algorithms are performed inparallel.

In a third set of specific examples, additional or alternative to thefirst and second, the collision detection algorithm is performedmultiple times, wherein information is continuously collected in S205and the extra information used in the subsequent calculations.

Additionally or alternatively, S210 can include any other suitableprocesses and/or be otherwise performed.

4.3 Method: Reconstructing the Collision S220

The method 200 includes reconstructing the collision S220, whichfunctions to determine features of the collision and/or informationrelevant to various entities (e.g., insurance entities, the driver,etc.) in light of the collision. As such, S220 can optionally functionto determine who and or what is at fault for an accident, facilitate theproduction of an output (e.g., materials, emergency response,communication, etc.) associated with the accident in S230, and/or canperform any other suitable function(s).

S220 is preferably performed in response to S210 and based on theoutputs produced in S210. Additionally or alternatively, S220 can beperformed as part of S210, in parallel with S210, in response to S205,and/or at any other suitable time(s).

S220 is preferably performed with a processing system of the system 100(e.g., the same processing system used in S210, a different processingsystem as that used in S210, etc.), but can additionally oralternatively be performed at any other suitable time(s). Additionallyor alternatively, S220 can be performed multiple times during the method200 (e.g., continuously, at a predetermined frequency, at randomintervals, etc.) and/or at any other suitable times.

S220 is preferably performed with a set of modules (e.g., as describedabove), further preferably with a set of modules implementing one ormore machine learning (e.g., traditional machine learning, deeplearning, etc.) approaches (equivalently referred to herein as machinelearning modules), such as one or more machine learning models and/oralgorithms. Additionally or alternatively, any or all of the machinelearning modules can implement one or more classical rule-basedapproaches (e.g., rule-based algorithms, decision trees, lookup tables,equations, etc.) and/or any combination of approaches.

In variations including multiple modules, each of the modules can beanalyzed independently, in combination with one or more other modules,in combination with all modules, and/or otherwise evaluated. Furtheradditionally or alternatively, the modules can be evaluated in any orall of: a serial fashion, a parallel fashion, and/or any combination. Inspecific examples, any or all of the modules are configured to beperformed (e.g., assigned) in an ordered or partially ordered fashion,wherein at least some of the modules are prioritized as being performedfirst. In these examples, the modules prioritized to be performed firstand preferably those which elicit emergency and/or otherwisetime-sensitive responses in S230, but can additionally or alternativelybe any other modules.

Additionally or alternatively, any or all of the modules can beselectively performed, such as if requested by certain entities (e.g.,by an insurance entity, by the driver, etc.), based on the outputs inS210, based on the inputs received in S205, and/or otherwise selectivelyperformed. Any or all of the modules can be performed a single time,multiple times, and/or any combination.

In variations including multiple modules, the modules can receive any orall of: the same inputs, different inputs, partially overlapping inputs,and/or any combination.

The set of modules preferably at least collectively receives as inputany or all of the set of outputs produced in S210. Additionally oralternatively, each of the set of modules can receive different outputsfrom S210 (e.g., confidence detection module receives outputs only fromcollision detection algorithm while severity detection modules receivesoutputs only from severity detection algorithm), each of the set ofmodules can receive all outputs from S210, any or all of the modules canreceive additional inputs (e.g., from S205, from the driver, etc.),and/or the modules can work with any other suitable information.

The set of modules preferably includes a confidence module (equivalentlyreferred to herein as a confidence detection module), which functions todetermine a confidence associated with the determination of a collisionin S210 (e.g., by the collision detection module). Additionally oralternatively, the confidence detection module can function to determinea confidence associated with any other and/or all algorithms, and/or anyother parameters. Further additionally or alternatively, the set ofmodules can include multiple confidence detection modules, be absent ofa confidence detection module, and/or include any other modules.

The confidence module is preferably performed first relative to any orall of the other modules, which can function to prompttime-sensitive/time-critical responses in S230 (e.g., automaticallycalling emergency services, notifying the driver or other user,automatically contacting roadside assistance, etc.) as early aspossible. Additionally or alternatively, the confidence detection modulecan be performed in parallel with other modules, after other modules,the emergency responses can be triggered based on outputs in S210,and/or the method can be otherwise suitably performed.

The confidence module is further preferably a machine learning model(e.g., traditional machine learning, deep learning, etc.), furtherpreferably a regression model, but can additionally or alternativelyinclude any other learned models, classical rule-based models (e.g.,mappings), and/or any combination.

The confidence module preferably receives as input at least the outputof a collision detection algorithm in S210, but can additionally oralternatively receive the outputs from any or all of the algorithms inS210 (e.g., severity detection algorithm, direction of impact detectionalgorithm, etc.), auxiliary information from S210 and/or S205, otherinputs (e.g., from S205), and/or any other suitable information. Theconfidence detection module preferably produces as an output aconfidence parameter (e.g., confidence value, confirmed “yes” or “no”determination, etc.) associated with the determination that a collisionhas occurred, but can additionally or alternatively produce any otheroutputs.

In variations in which the collision detection algorithm is performedmultiple times, the confidence detection module can optionally beperformed multiple times, such as each time the collision detectionalgorithm is performed. Additionally or alternatively, the confidencedetection module can be performed once (e.g., after the last iterationof the collision detection algorithm) and/or otherwise performed.

Additionally or alternatively, the confidence module can be performedmultiple times (e.g., as described for the collision detection algorithmabove) independent of how many times the collision detection algorithmis performed, a single time, a dynamically determined number of times(e.g., continuously until a threshold confidence level is reached,continuously until the confidence falls below a threshold and the methodis terminated, etc.), and/or any other number of times.

The set of modules further preferably includes a severity module(equivalently referred to herein as a severity detection module), whichfunctions to determine a severity associated with the collision. Thiscan in turn function to help an insurance entity assess the collision(e.g., perform a claim adjustment), help the driver get properlyreimbursed, inform the driver and/or other entities of next steps,recommend an appropriate body shop to the driver depending on the extentof the damage, call an ambulance (e.g., automatically by the processingsystem, from the user's mobile device, etc.), call a tow truck (e.g.,automatically by the processing system, from the user's mobile device,etc.), and/or can function to perform any other suitable functions.

The severity module is preferably performed after (e.g., and offline,and online, etc.) the confidence detection module, and optionally inresponse to a request from one or more entities, but can additionally oralternatively be performed at any suitable time(s).

The severity module is preferably a machine learning model (e.g.,traditional machine learning, deep learning, etc.), further preferably aregression model, but can additionally or alternatively include anyother learned models, classical rule-based models (e.g., mappings),and/or any combination. The severity detection module preferablyreceives as input at least an output of a severity detection algorithmin S210, but can additionally or alternatively receive the outputs fromany or all of the algorithms in S210 (e.g., collision detectionalgorithm, direction of impact detection algorithm, etc.), auxiliaryinformation from S210 and/or S205 (e.g., driver's location information,additional location information showing that the driver went to an autobody shop, additional location information showing that driver went to ahospital, etc.) other inputs (e.g., from S205), and/or any othersuitable information.

The severity module can produce any number of outputs, such as, but notlimited to, any or all of: one or more scores (e.g., severity score,particular type of severity score, etc.), an indication of whether ornot physical damage to the vehicle has occurred and/or is suspected tohave occurred (e.g., fender bender damage, more serious damage, totaledvs. not totaled, estimated cost of repairs, property damage only [PDO]conclusion, etc.); an indication of whether or not injury to a driverand/or passenger and/or pedestrian has occurred and/or is suspected tohave occurred (e.g., fatal injury, minor injury, no injury, etc.),and/or any other suitable outputs. The outputs for this module (and/orany other module) can be any or all of: quantifiable (e.g., severityvalues), qualitative (e.g., binary, ranking, etc.), any other output,and/or any combination.

Additionally or alternatively, the set of modules can be absent of aseverity module and/or can include any other modules.

The set of modules further preferably includes a direction of impactdetection module, which functions to determine the direction of impactof the vehicle associated with the driver (and/or another vehicle in thecollision). This can, in turn, function to determine if the driver is atfault in the collision (e.g., if the driver has frontal damageindicating that he or she rear ended a vehicle).

The directions of impact preferably include those described above, butcan additionally or alternatively include any other directions ofimpact.

The direction of impact module is preferably performed after (e.g., andoffline, and online, etc.) the confidence detection module, andoptionally in response to a request from one or more entities, but canadditionally or alternatively be performed at any suitable time(s).

The direction of impact module is preferably a machine learning model(e.g., traditional machine learning, deep learning, etc.), furtherpreferably a regression model, but can additionally or alternativelyinclude any other learned models, classical rule-based models (e.g.,mappings), and/or any combination. The direction of impact modulepreferably receives as input at least an output of a direction of impactdetection algorithm in S210, but can additionally or alternativelyreceive the outputs from any or all of the algorithms in S210 (e.g.,collision detection algorithm, severity detection algorithm, etc.),auxiliary information from S210 and/or S205 (e.g., driver's locationinformation, additional location information showing that the driverwent to an auto body shop, additional location information showing thatdriver went to a hospital, etc.) other inputs (e.g., from S205), and/orany other suitable information.

The direction of impact detection module can produce any number ofoutputs, such as the particular direction or directions of impact(and/or equivalently the locations of impact) on the vehicle.Additionally or alternatively, the directions/locations of impact onanother vehicle can be determined, and/or any other suitable outputs canbe produced. The outputs for this module (and/or any other module) canbe any or all of: quantifiable (e.g., severity values), qualitative(e.g., particular direction/location, binary, ranking, etc.), any otheroutput, and/or any combination.

Additionally or alternatively, the set of modules can be absent of adirection of impact module and/or can include any other suitablemodules.

The set of modules preferably includes a fraud module, wherein the fraudmodule is configured to detect and/or determine (e.g., determine aperpetrator of, determine a type of, etc.) fraud associated with thecollision. The fraud can be associated with fraud in which the driver isthe victim, fraud in which the driver is the perpetrator, and/or anycombination. The fraud can include, for instance, any or all of: astaged auto accident (e.g., driver forced another driver into acollision, driver braked suddenly to have driver behind rear end him,driver suddenly accelerated to hit another vehicle that he had motionedin, swoop and squat staged accident, drive down staged accident, panicstop staged accident, sideswipe staged accident, etc.); amisrepresentation of damages (e.g., extra damages claimed aftercollision); an uninsured driver being covered for by an insuredpassenger (e.g., as detected based on the driver's driving historyand/or driving behavior and/or driving style); and/or any otherfraudulent activities.

The fraud module preferably detects fraud based on a classical,rule-based model, but can additionally or alternatively implement one ormore machine learning models (e.g., traditional machine learning, deeplearning, etc.), and/or any other processes.

The fraud module is preferably performed after (e.g., and offline, andonline, etc.) the confidence detection module, and optionally inresponse to a request from one or more entities (e.g., insurancecompany, police, etc.), but can additionally or alternatively beperformed at any suitable time(s).

The fraud module preferably receives as any or all of the outputsproduced S210 and further preferably additional auxiliary information(e.g., driver's locations after collision, driver's mobile device usagebefore and/or during and/or after collision, driver's auto body shopbills after collision, etc.), but can additionally or alternativelyreceive a subset of this information as inputs, other inputs (e.g., fromS205), and/or any other suitable information.

In some variations, the fraud module can include collecting information(e.g., in S205, after the collision, etc.) associated with anotherdriver involved in the collision (and/or any other users such aspassengers and/or pedestrians), which can function to bolster theoutputs of the fraud module (e.g., by looking into another driver'sbehavior involved in crash).

The fraud module can produce any number of outputs, such as, but notlimited to, any or all of: one or more probabilities (e.g., that thedriver was involved in fraud, that another driver was involved in fraud,etc.), an identification of an individual and/or individuals involved infraud, a confidence value (e.g., associated with a fraud determination),a type of fraud suspected, and/or any other suitable outputs.

Additionally or alternatively, the set of modules can be absent of afraud detection module and/or can include any other suitable modules.

S220 can additionally or alternatively include any other suitablemodules configured to determine any suitable outputs and performed inany suitable ways.

In a first variation, S220 includes evaluating a set of modules based onthe outputs of the set of algorithms in S210 (and optionally any otherinformation), wherein the set of modules includes any or all of aconfidence module, a fraud module, a direction of impact module, aseverity module, and/or any other suitable modules.

In a first example, the confidence module, direction of impact module,and the severity module are each machine learning regression models,wherein the fraud module is a classic rule-based model.

In a specific example, additional or alternative to the first, the setof modules are organized into a set of temporal groups, wherein the1^(st) group includes the confidence module and is performed first(e.g., to reduce the time needed to elicit an emergency response) andthe 2^(nd) group includes remaining modules and is performed at one ormore later times (and optionally offline and/or in response to therequest from an entity and/or once additional information such as theuser's future location is collected).

In a third example, additional or alternative to the above examples,each of the modules receives the same set of outputs from S210 asinputs. In an alternative examples, any or all of the modules canreceive different inputs relative to each other.

Additionally or alternatively, S220 can include any other processesand/or be otherwise performed.

4.4 Method: Producing an Output Based on the Reconstruction S230

The method 200 preferably includes producing an output based on thereconstruction S230, which functions to present a set of one or morefeatures of an accident to a user or group (e.g., insurance agent, legalentity, law enforcement personnel, etc.), thereby providing key insightsregarding the accident to create actionable insight. Additionally oralternatively, S230 can function to train one or more models and/ormodules, predict and/or prevent the occurrence of an accident (e.g.,through a notification to a driver in an accident-prone environment),and/or perform any other suitable function(s).

The output is preferably produced in response to S220, furtherpreferably in response to processing the modules in S220, but canadditionally or alternatively be produced in response to S210 (e.g.,based on one or more classifiers), in response to a user request, uponconfirmation of an accident, upon request by an entity, throughout themethod 200, and/or at any other suitable time(s) and in response to anysuitable triggers during the method 200. In some variations, forinstance, data presented in the reconstruction output includes theoutputs of one or more modules, auxiliary information (e.g., the vehiclespeed just prior to the collision and a set of gravitational forceequivalent [g-force and/or G-force] values involved in the collision)produced in S210 and/or prior to S210, optionally outputs from one ormore classifiers in S210, and/or any other information produced at anysuitable times during the method 200. In additional or alternativevariations in which S230 involves the creation of an insurance claim,the insurance claim can be triggered and/or created and/or verifiedthroughout the method 200.

The reconstruction output can include any or all of: a report/document(e.g., typed report, display, presentation, slide deck, video, audiorecording, insurance claim, auto body report, etc.); a visualization(e.g., animated visualization of collision) such as a collisionreconstruction dashboard (e.g., as shown in FIGS. 9A-9G); a score (e.g.,updated driver score, updated route score such as a route risk score,etc.); a notification (e.g., text message, email, etc.); a communication(e.g., an automated call to an emergency responder, an automated call tothe driver, etc.); and/or any other suitable output(s).

In some variations, the reconstruction output includes any or all of aninsurance claim (e.g., as shown in FIG. 8B), wherein the method caninclude any or all of: triggering the creation of (e.g., initiating) aclaim (e.g., in response to detecting a collision, in the form of anFNOL, etc.); updating a claim with additional information received, suchas through repeated instances of S205 (e.g., subsequently identifiedlocations and/or trips of the driver, follow-up with driver such asthrough notifications/messages and/or calls, etc.); accessing detailedcollision information (e.g., collected in S205, determined throughoutthe method 200, etc.); producing a claim and/or claim recommendationbased on any or all of the above; and/or any other suitable processes.In such variations, any or all of the information (e.g., collisioninformation, historical driver data, etc.) in the method 200 canoptionally be shared with an insurance entity, such as through any orall of: webhooks, APIs, and/or any other suitable processes and/ortools.

In a first set of examples of creating an insurance claim, the methodincludes automatically (e.g., zero touch/zero-click) and/or partiallyautomatically (e.g., one touch) performing a claim registration process(e.g., as shown in FIG. 8A), which functions to automatically notify thedriver's insurer that the driver was involved in a collision and/or toautomatically create a claim in the insurer system. This can confer abenefit to the driver of not having to initiate this process himself orherself. This can also confer any number of benefits to claims adjusters(e.g., human claims adjusters, bots, etc.), such as, but not limited to,any or all of: enabling the claims adjuster to retrieve data associatedwith a driver (e.g., who has reported a claim) such as historicaldriving data (e.g., to be used for further analysis to identify a riskassociated with the driver) and/or collision data; and/or any otherbenefits. Automatically notifying the insurer can optionally beperformed with one or more third party applications and/or other thirdparty tools (e.g., third party client applications executing on theuser's mobile device and in communication with an SDK and/or applicationprogramming interface [API] collecting any or all of the inputs in S205)such as an insurance carrier application and/or tool configured toreceive inputs from the system 100, the method 200, and/or from theuser. In a particular specific example, the claim registration processis a one touch process in which the driver enters inputs (e.g., severityof collision input, choice or whether to report a claim or ignore it,etc.) into an insurance carrier application, wherein in response to theinputs, the insurance carrier application can optionally retrieve dataassociated with the collision and/or otherwise trigger a claimgeneration process. In another particular specific example, a thirdparty tool can automatically retrieve data associated with the collision(e.g., from the SDK, from another client application, from storage, froma remote computing system and/or remote server, etc.), wherein the thirdparty tool is responsible for creating the claim.

The reconstruction output can additionally or alternatively trigger oneor more actions and/or responses, such as any or all of: the contactingof emergency assistance (e.g., police, ambulance, hospital dispatch,etc.); the contacting of the driver (e.g., through notifications asdepicted in FIGS. 6A-6B, through notifications as depicted in FIGS.7A-7B); the determination and/or adjustment of one or more insurancecalculations and/or hypotheses (e.g., claims adjustment, determinationof who is at fault, determination of damage extent of vehicle,initiating a FNOL report, etc.); and/or any other suitable actionsand/or responses.

S230 can additionally or alternatively include determining (e.g.,training) one or more models based on any or all of the outputs producedin the method, such as an insurance model (e.g., to automaticallyproduce insurance outputs) and/or any other suitable models.

Additionally or alternatively, S230 can produce one or more insights(e.g., based on aggregated collision information) related to collisions,such as which factors contribute to (e.g., correlate with) collisionsand/or how factors contribute to collisions, wherein the factors caninclude, for instance, any or all of: region in which the driver isdriving (e.g., geographical region, road type, etc.), demographicinformation of driver (e.g., gender, age, size, etc.), vehicleinformation (e.g., vehicle make/model, vehicle size, etc.), and/or anyother suitable information. In specific examples, for instance, datacollected in the method indicating that a particular vehicle make/modelhas a particularly high incidence of frontal damage (e.g., due to sideswiping other vehicles) can be used to determine that said vehicles havea particularly large blind spot.

S230 can additionally or alternatively be used to determine and/orupdate one or more scores associated with any or all of: a driver (e.g.,through updating a driver risk score in an event of a collision), a roadsegment and/or road type, and/or any other suitable information. In somevariations, for instance, S230 is used to update the risk scoreassociated with particular road segments (equivalently referred toherein as route segments) associated with collisions (e.g., anycollisions, collisions above a predetermined threshold, collisions abovea predetermined threshold relative to number of miles driven on roadsegment, etc.). In specific examples, for instance, the risk associatedwith a route segment as determined in U.S. application Ser. No.17/111,299, filed 3 Dec. 2020, which is incorporated herein in itsentirety by this reference, can be updated based on any or all of theoutputs determined in the method 200.

In a first set of variations, S230 includes generating a presentationproductized as a UI for a user and/or entity, such as an insurance firm,the driver, law enforcement personnel, and/or any other user or entity.

In a second set of variations, S230 includes generating a report as partof a service for a user or entity, such as an insurance firm, thedriver, law enforcement personnel, and/or any other user or entity. Inspecific examples, S230 includes providing a report to a claims adjusterwhich functions to ensure that the claim is commensurate with thecollision.

In a third set of variations, S230 includes triggering an emergencyresponse based on the output of the confidence module in S220 and/or anyor all of the other modules.

In a fourth set of variations, S230 includes triggering an emergencyresponse based on the output of the collision detection algorithm inS210 and/or any or all of the other algorithms.

Additionally or alternatively, S230 can include any other suitableprocesses.

4.5 Method: Training One or More Models and/or Modules

The method 200 can optionally include training any or all of the modelsand/or modules described above (e.g., as shown in FIG. 5), which canfunction to enable full or partial automation of any or all of themodels and modules, improve the performance of any or all of the modelsand modules, enable any or all of the models and modules to continue tolearn based on new and/or continuously collected data, and/or canperform any other suitable functions.

The training can be performed at any or all of: prior to S205, prior toS210, prior to S220, prior to S230, after S230 (e.g., as part of aretraining and/or updating of any or all of the modules and/or models),and/or at any other suitable times. Additionally or alternatively,training can occur multiple times during the method and/or the methodcan be performed in absence of training and/or retraining.

In some variations, a first training of one or more models and/ormodules is performed prior to the method 200, wherein any or all of thetrained models and/or modules is updated (retrained) based on theinformation collected in the method 200 and any or all of the outputsdetermined based on the information.

The information used to train the models and/or modules preferablyincludes historical data collected from previous drivers, such as any orall of the set of inputs collected in S205. The historical preferablyincludes at least sensor data collected from the drivers, furtherpreferably at mobile devices (e.g., smartphones) associated with thedrivers, and optionally any or all outputs indicating whether or not acollision occurred (e.g., outputs produced in S230, model outputs,module outputs, etc.) such as any or all of those described in themethod (e.g., location and/or behavior information of the drivers postcollision, driver confirmation in response to sending the driver anotification, etc.) and/or any other suitable data (e.g., fromcontacting drivers after collision for follow-up, from receivingconfirmation from drivers regarding a potential collision, frominsurance reports, from police reports, from auto body shop receipts,from interviews with any entities, etc.).

The information used to train any or all of the models and/or modulescan additionally or alternatively include crash test data (e.g., tocorrelate sensor data with different types of collisions and/orcollision features such as severity and/or direction of impact, etc.),data from one or more OBD ports of the vehicle and/or from one or moreOEM components (e.g., during crash tests, on the road, etc.), and/or anyother suitable information.

The training can additionally or alternatively include any number ofhuman-in-the-loop analyses (e.g., during supervised learning), which canfunction, for instance, to implement human knowledge and/or otheroutside information—such as any or all of: police reports generatedafter accidents, insurance claims generated after accidents, auto bodyinformation generated after accidents, fraud characteristics, and/or anyother suitable information—to train for accurate and usefulclassifications.

Additionally or alternatively, the training can include any othersuitable processes.

4.6 Variations

In a first variation of the method 200, the method includes: collectinginformation S205 from a set of one or more mobile devices (e.g., via anSDK), wherein the information is collected continuously at least whilethe vehicle is driving, and wherein the information includes at leastmotion information (e.g., speed, acceleration, etc.) and locationinformation (e.g., GPS coordinates, route information, etc.) andoptionally driver information (e.g., risk score, historical drivingperformance, mobile device usage, etc.) and/orcircumstantial/environmental information (e.g., as shown in FIG. 5);detecting a collision and/or one or more collision features with acollision model based on the information; reconstructing the collisionwith a set of modules S220, wherein a subset of modules is optionallyselected based on the results of the collision model; and producing anoutput based on the reconstructed collision, wherein the output caninclude any or all of: a presentation and/or report summarizing keyfeatures of the accident which can provide actionable insight regardingthe accident, an action (e.g., triggering an emergency response,transmitting a notification to the driver, etc.), a communication,and/or any other suitable outputs. Additionally or alternatively, themethod 200 can include any other suitable processes (e.g., training anyor all of the models and modules, retraining any or all of the modelsand modules, etc.).

In a first specific example, the collision model includes a set ofclassifier algorithms, wherein the set of classifier algorithms caninclude any or all of: a collision detection algorithm, a severitydetection algorithm, a direction of impact algorithm, and/or any othersuitable algorithms, and wherein the set of modules includes acombination of machine learning and classical modules, wherein the setof modules can include any or all of: a confidence module, a fraudmodule, a direction of impact module, and a severity module. Theclassifier algorithms preferably implement a combination of traditionalmachine learning (e.g., gradient boosting machines) and deep learning,and the machine learning modules preferably implement regressionlearning, but the models and/or modules can additionally oralternatively implement any other suitable models.

In a second variation of the method 200, the method includes: collectinginformation S205 from a set of one or more mobile devices (e.g., via anSDK), wherein the information is collected continuously at least whilethe vehicle is driving, and wherein the information includes at leastmotion information (e.g., speed, acceleration, etc.) and locationinformation (e.g., GPS coordinates, route information, etc.) andoptionally driver information (e.g., risk score, historical drivingperformance, mobile device usage, etc.) and/orcircumstantial/environmental information; detecting a collision andreconstructing the collision based on one or both of a collision modeland/or set of modules; and producing an output based on thereconstructed collision, wherein the output can include any or all of: apresentation and/or report summarizing key features of the accidentwhich can provide actionable insight regarding the accident, an action(e.g., triggering an emergency response, transmitting a notification tothe driver, etc.), a communication, and/or any other suitable outputs.Additionally or alternatively, the method 200 can include any othersuitable processes (e.g., training any or all of the models and modules,retraining any or all of the models and modules, etc.).

In a third variation of the method 200, the method includes: collectinginformation S205 from onboard a vehicle (e.g., from sensors mounted tothe vehicle, from an OBD port of the vehicle) and optionally from a setof one or more mobile devices (e.g., via an SDK), wherein theinformation is collected continuously at least while the vehicle isdriving, and wherein the information includes at least motioninformation (e.g., speed, acceleration, etc.) and location information(e.g., GPS coordinates, route information, etc.) and optionally driverinformation (e.g., risk score, historical driving performance, mobiledevice usage, etc.) and/or circumstantial/environmental information;detecting a collision and/or one or more collision features with acollision model based on the information; reconstructing the collisionwith a set of modules S220, wherein a subset of modules is optionallyselected based on the results of the collision model; and producing anoutput based on the reconstructed collision, wherein the output caninclude any or all of: a presentation and/or report summarizing keyfeatures of the accident which can provide actionable insight regardingthe accident, an action (e.g., triggering an emergency response,transmitting a notification to the driver, etc.), a communication,and/or any other suitable outputs. Additionally or alternatively, themethod 200 can include any other suitable processes (e.g., training anyor all of the models and modules, retraining any or all of the modelsand modules, etc.).

Additionally or alternatively, the method 200 can include any othersuitable processes performed in any suitable order.

Although omitted for conciseness, the preferred embodiments includeevery combination and permutation of the various system components andthe various method processes, wherein the method processes can beperformed in any suitable order, sequentially or concurrently.

As a person skilled in the art will recognize from the previous detaileddescription and from the figures and claims, modifications and changescan be made to the preferred embodiments of the invention withoutdeparting from the scope of this invention defined in the followingclaims.

We claim:
 1. A method for collision detection and reconstruction, themethod comprising: at a remote computing system in communication with amobile device associated with a driver, wherein the driver is arrangedwithin a vehicle, receiving a set of inputs, wherein the set of inputscomprises: a location dataset collected at a location sensor of themobile device; a motion dataset collected at a motion sensor the mobiledevice; a pressure dataset collected at a pressure sensor of the mobiledevice; processing the set of inputs with a model, wherein the model isa machine learning model, wherein at least a portion of the machinelearning model is a deep learning model, wherein the model comprises aset of algorithms configured to produce a set of probabilistic outputs,the set of algorithms comprising: a collision detection algorithmconfigured to produce a collision parameter; a severity detectionalgorithm configured to produce a severity parameter; and a direction ofimpact detection algorithm configured to produce a direction of impactparameter; processing a set of modules based on the set of probabilisticoutputs to determine a set of module outputs, wherein the set of modulescomprises: a confidence module; a fraud module; a direction of impactmodule; and a severity module; implementing an emergency response basedon at least one of the collision detection algorithm and the confidencemodule; providing a subset of module outputs to an insurance entity,wherein the subset of module outputs comprises outputs from at least oneof the fraud module, the direction of impact module, and the severitymodule; and updating at least one of the set of algorithms and the setof modules based on the set of module outputs; further comprisingdetermining a collision risk score associated with a road segment basedon at least one of the set of module outputs, wherein the road segmentis arranged proximal to the location of the collision.
 2. The method ofclaim 1, further comprising processing the collision detection algorithmmultiple times.
 3. The method of claim 2, wherein each of the severitydetection algorithm and the direction of impact detection algorithm isperformed a single time.
 4. The method of claim 1, wherein a firstsubset of the set of modules comprises machine learning models andwherein a second subset of the set of modules comprises classicalrule-based models.
 5. The method of claim 4, wherein the second subsetcomprises the fraud module.
 6. The method of claim 1, furthercomprising: receiving a second set of inputs associated with a seconddriver; and processing the second set of inputs with an updated set ofalgorithms, the updated set of algorithms determined based on the firstdriver.
 7. The method of claim 1, wherein a second portion of the modelimplements traditional machine learning.
 8. The method of claim 7,wherein the traditional machine learning comprises a gradient boostingmachine.
 9. The method of claim 1, wherein the model is furtherconfigured to produce as outputs a set of auxiliary outputs, wherein theset of auxiliary outputs comprises a set of gravitational forceequivalent values and a speed of the vehicle just before the collision.